README.md

metadata

title: Bangla Book Recommender
emoji: 📚
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: false
license: cc-by-nc-4.0
short_description: Cold-start recommendations from 127K Bangla books
tags:
  - recommendation
  - bangla
  - lightgcn
  - two-tower

📚 Bangla Book Recommender

A cold-start book recommendation interface for the RokomariBG dataset — the first large-scale multi-entity heterogeneous graph dataset for Bangla book recommendation, built from publicly scraped reviews and metadata on Rokomari.com, Bangladesh's largest online bookstore.

The Space lets a visitor pick a few books they have enjoyed and receive recommendations from either of two benchmarked models from the paper:

Model	Notes
Neural Two-Tower	Best benchmarked model. Item tower fuses ID, content (title, summary, author, publisher), and metadata (price, rating, pages); user tower combines a user-ID embedding with pooled history. Strongest at cold-start. Covers all 127K books.
LightGCN	Pure graph collaborative filtering with 4 GCN layers and uniform layer averaging. Trained on the 16K-book subset with sufficient interaction history.

How recommendation works (cold-start mode)

A new visitor has no user_id in the trained models. So instead of looking up a stored user embedding, the Space derives one on the fly:

1. The visitor selects N books they have enjoyed (Bangla or English).
2. Each model has a precomputed book embedding for every book in its training catalogue.
3. The visitor's taste vector is computed as the L2-normalised mean of the selected books' embeddings.
4. Cosine similarity is computed between the taste vector and every book embedding. The seed books are masked out, and the top-K nearest books are returned.
5. Liked recommendations can be promoted into the seed set, after which re-running yields progressively more personalised results.

The pipeline is pure NumPy at inference time — no model is loaded at runtime, only the precomputed embeddings — so the Space is fast and cheap to host on the free CPU tier.

File layout

All artefacts live in the Space root:

.
├── app.py                       # Gradio app
├── requirements.txt
├── README.md                    # this file
├── books_metadata.parquet       # title, author, category, rating, summary, url
├── two_tower_book_emb.npy       # (N, 256) float32, L2-normalisable
├── two_tower_book_ids.npy       # (N,)     book_id strings, row-aligned
├── lightgcn_book_emb.npy        # (16266, 64) float32, L2-normalisable
└── lightgcn_book_ids.npy        # (16266,)    book_id strings, row-aligned

Citation

@misc{ahmed2026personalizedbanglabookrecommendation,
  title  = {Towards Personalized Bangla Book Recommendation:
            A Large-Scale Multi-Entity Book Graph Dataset},
  author = {Rahin Arefin Ahmed and Md. Anik Chowdhury and
            Sakil Ahmed Sheikh Reza and Devnil Bhattacharjee and
            Muhammad Abdullah Adnan and Nafis Sadeq},
  year   = {2026},
  eprint = {2602.12129},
  archivePrefix = {arXiv},
  primaryClass  = {cs.IR},
  url    = {https://arxiv.org/abs/2602.12129}
}

Links

• 📄 Paper: https://arxiv.org/abs/2602.12129
• 💾 Dataset: https://huggingface.co/datasets/DevnilMaster1/Bangla-Book-Recommendation-Dataset
• 🐙 Code: https://github.com/DevnilMaster/Bangla-Book-Recommendation-Dataset

License: CC BY-NC 4.0 (matches the dataset licence)